{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to check the quality of data from vitaldb dataset\n",
    "In this example, we will learn how to check the quality of data from <code>vitaldb</code> dataset.\n",
    "\n",
    "> Note that <b>all users who use Vital DB, an open biosignal dataset, must agree to the Data Use Agreement below. \n",
    "</b> If you do not agree, please close this window. \n",
    "Click here: [Data Use Agreement](https://vitaldb.net/dataset/?query=overview&documentId=13qqajnNZzkN7NZ9aXnaQ-47NWy7kx-a6gbrcEsi-gak&sectionId=h.vcpgs1yemdb5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-24T03:18:22.017694Z",
     "start_time": "2020-11-24T02:42:19.417403Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "4150    647     3.7     2.8%    1.0     1.8%    1.3     1.7%\t\t\t\t\t\t\t\n",
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      "4264    634     4.6     4.1%    8.3     12.7%   6.6     8.0%\t\t\t\t\t\t\t\n",
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      "4278    381     2.5     2.1%    6.1     9.1%    4.0     4.5%\t\t\t\t\t\t\t\n",
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      "4280    295     5.3     4.2%    7.3     12.3%   6.3     7.5%\t\t\t\t\t\t\t\n",
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      "4283    716     1.7     1.5%    3.3     6.9%    2.3     3.4%\t\t\t\t\t\t\t\n",
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      "4316    357     5.1     4.3%    2.1     3.9%    2.6     3.3%\t\t\t\t\t\t\t\n",
      "4317    290     6.2     6.1%    11.9    20.9%   7.5     9.9%\t\t\t\t\t\t\t\n",
      "4320    547     3.6     2.9%    1.1     2.2%    1.6     2.1%\t\t\t\t\t\t\t\n",
      "4322    453     3.8     3.1%    1.6     2.6%    1.7     2.0%\t\t\t\t\t\t\t\n",
      "4325    373     4.0     3.5%    1.7     2.7%    1.5     1.8%\t\t\t\t\t\t\t\n",
      "4326    306     8.1     6.4%    4.2     6.7%    2.0     2.3%\t\t\t\t\t\t\t\n",
      "4327    222     6.2     5.3%    8.0     15.3%   7.2     9.7%\t\t\t\t\t\t\t\n",
      "4328    404     2.5     2.3%    5.1     10.3%   2.7     4.1%\t\t\t\t\t\t\t\n",
      "4332    135     5.5     4.1%    8.4     12.9%   7.3     8.0%\t\t\t\t\t\t\t\n",
      "4333    347     8.3     7.4%    3.0     4.8%    4.4     5.4%\t\t\t\t\t\t\t\n",
      "4335    535     3.9     3.5%    1.4     2.2%    2.1     2.5%\t\t\t\t\t\t\t\n",
      "4337    187     7.9     7.3%    4.9     9.4%    5.9     8.3%\t\t\t\t\t\t\t\n",
      "4339    181     7.9     7.3%    4.2     7.5%    5.4     7.3%\t\t\t\t\t\t\t\n",
      "4341    874     7.1     5.4%    2.8     4.9%    4.1     5.2%\t\t\t\t\t\t\t\n",
      "4345    394     3.8     3.5%    5.9     11.5%   5.2     7.8%\t\t\t\t\t\t\t\n",
      "4347    760     3.4     3.3%    6.9     12.2%   5.2     6.9%\t\t\t\t\t\t\t\n",
      "4350    197     3.7     2.7%    11.1    15.0%   4.3     4.2%\t\t\t\t\t\t\t\n",
      "4352    134     3.5     2.5%    5.2     7.8%    4.0     4.2%\t\t\t\t\t\t\t\n",
      "4354    277     5.2     4.3%    8.0     11.1%   6.5     7.1%\t\t\t\t\t\t\t\n",
      "4356    486     7.0     6.3%    2.8     5.3%    4.3     5.7%\t\t\t\t\t\t\t\n",
      "4362    307     4.1     3.4%    3.5     6.2%    1.4     1.8%\t\t\t\t\t\t\t\n",
      "4364    290     7.6     6.5%    12.5    21.4%   9.7     11.6%\t\t\t\t\t\t\t\n",
      "4367    464     5.3     4.5%    2.0     3.6%    2.6     3.4%\t\t\t\t\t\t\t\n",
      "4368    440     6.7     4.8%    1.7     2.9%    2.6     3.0%\t\t\t\t\t\t\t\n",
      "4371    459     8.3     6.0%    3.1     4.5%    4.4     4.8%\t\t\t\t\t\t\t\n",
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      "4377    927     7.8     6.1%    4.2     6.2%    5.6     6.4%\t\t\t\t\t\t\t\n",
      "4380    144     3.1     2.4%    7.0     11.2%   3.6     4.3%\t\t\t\t\t\t\t\n",
      "4382    355     3.9     3.5%    1.3     2.2%    1.6     2.0%\t\t\t\t\t\t\t\n",
      "4383    524     6.3     5.7%    3.3     6.6%    4.4     6.2%\t\t\t\t\t\t\t\n",
      "4385    432     6.8     6.8%    3.4     6.4%    4.5     6.2%\t\t\t\t\t\t\t\n",
      "4386    512     3.3     2.5%    4.7     7.3%    4.1     4.7%\t\t\t\t\t\t\t\n",
      "4387    223     8.7     7.1%    5.3     8.4%    5.7     6.6%\t\t\t\t\t\t\t\n",
      "4388    260     5.4     5.2%    2.5     4.0%    2.9     3.7%\t\t\t\t\t\t\t\n",
      "4389    346     2.7     2.3%    3.9     6.2%    1.8     2.2%\t\t\t\t\t\t\t\n",
      "4390    336     4.6     3.8%    7.7     12.5%   6.1     7.4%\t\t\t\t\t\t\t\n",
      "4392    390     4.7     3.7%    8.0     13.2%   6.6     7.5%\t\t\t\t\t\t\t\n",
      "4396    264     6.6     4.4%    9.1     11.2%   7.2     6.7%\t\t\t\t\t\t\t\n",
      "4399    134     3.2     2.8%    6.7     12.2%   4.1     5.4%\t\t\t\t\t\t\t\n",
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      "4402    474     4.6     4.2%    1.4     2.4%    1.9     2.5%\t\t\t\t\t\t\t\n",
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      "4406    268     6.8     5.4%    9.4     16.3%   6.6     8.3%\t\t\t\t\t\t\t\n",
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      "4411    345     7.8     7.3%    4.3     8.3%    5.4     7.4%\t\t\t\t\t\t\t\n",
      "4414    112     2.5     1.8%    5.9     9.2%    4.0     4.4%\t\t\t\t\t\t\t\n",
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      "4425    260     2.6     1.9%    2.8     3.9%    0.6     0.7%\t\t\t\t\t\t\t\n",
      "4426    249     4.7     3.9%    2.1     3.3%    2.8     3.3%\t\t\t\t\t\t\t\n",
      "4428    386     8.3     6.2%    4.8     7.8%    5.8     6.6%\t\t\t\t\t\t\t\n",
      "4429    638     3.8     2.6%    1.0     2.0%    1.6     2.0%\t\t\t\t\t\t\t\n",
      "4430    429     3.6     2.8%    3.2     5.0%    1.5     1.7%\t\t\t\t\t\t\t\n",
      "4432    466     4.1     3.2%    1.7     2.8%    1.6     1.8%\t\t\t\t\t\t\t\n",
      "4433    211     1.5     1.1%    3.7     5.0%    2.4     2.5%\t\t\t\t\t\t\t\n",
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     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import vitaldb\n",
    "import os\n",
    "#import matplotlib.pyplot as plt\n",
    "\n",
    "df_trks = pd.read_csv(\"https://api.vitaldb.net/trks\")\n",
    "\n",
    "# find cases which have both ART_MBP and SNUADC/ART\n",
    "caseids = list(\n",
    "    set(df_trks[df_trks['tname'] == 'Solar8000/ART_SBP']['caseid']) & \n",
    "    set(df_trks[df_trks['tname'] == 'Solar8000/ART_DBP']['caseid']) & \n",
    "    set(df_trks[df_trks['tname'] == 'Solar8000/ART_MBP']['caseid']) & \n",
    "    set(df_trks[df_trks['tname'] == 'SNUADC/ART']['caseid'])\n",
    ")\n",
    "\n",
    "all_sbps = []\n",
    "all_dbps = []\n",
    "all_mbps = []\n",
    "all_art_sbps = []\n",
    "all_art_dbps = []\n",
    "all_art_mbps = []\n",
    "\n",
    "output = 'caseid\\tlen\\tsbp\\tsbp%\\tdbp\\tdbp%\\tmbp\\tmbp%\\n'\n",
    "print(output, end='')\n",
    "\n",
    "fo = open('qa.tsv', 'w')\n",
    "fo.write(output)\n",
    "\n",
    "for caseid in caseids:\n",
    "    if caseid < 4640:\n",
    "        continue\n",
    "        \n",
    "    sbp_tid = df_trks[(df_trks['caseid'] == caseid) & (df_trks['tname'] == 'Solar8000/ART_SBP')]['tid'].values[0]\n",
    "    dbp_tid = df_trks[(df_trks['caseid'] == caseid) & (df_trks['tname'] == 'Solar8000/ART_DBP')]['tid'].values[0]\n",
    "    mbp_tid = df_trks[(df_trks['caseid'] == caseid) & (df_trks['tname'] == 'Solar8000/ART_MBP')]['tid'].values[0]\n",
    "    art_tid = df_trks[(df_trks['caseid'] == caseid) & (df_trks['tname'] == 'SNUADC/ART')]['tid'].values[0]\n",
    "\n",
    "    SRATE = 100\n",
    "    SEGLEN = 30\n",
    "    vals = vitaldb.dataset.load_trks([sbp_tid, dbp_tid, mbp_tid, art_tid], 1 / SRATE)\n",
    "\n",
    "    # extract segements\n",
    "    case_sbps = []\n",
    "    case_dbps = []\n",
    "    case_mbps = []\n",
    "    case_art_sbps = []\n",
    "    case_art_dbps = []\n",
    "    case_art_mbps = []\n",
    "\n",
    "    raw_sbps_list = []\n",
    "    raw_dbps_list = []\n",
    "    raw_mbps_list = []\n",
    "    raw_arts_list = []\n",
    "    for i in range(0, len(vals) - SEGLEN * SRATE, SEGLEN * SRATE):\n",
    "        raw_sbps = vals[i:i + SRATE * SEGLEN, 0]\n",
    "        raw_dbps = vals[i:i + SRATE * SEGLEN, 1]\n",
    "        raw_mbps = vals[i:i + SRATE * SEGLEN, 2]\n",
    "        raw_arts = vals[i:i + SRATE * SEGLEN, 3]\n",
    "\n",
    "        sbps = raw_sbps[~np.isnan(raw_sbps)]\n",
    "        dbps = raw_dbps[~np.isnan(raw_dbps)]\n",
    "        mbps = raw_mbps[~np.isnan(raw_mbps)]\n",
    "        arts = raw_arts[~np.isnan(raw_arts)]\n",
    "\n",
    "        # exclude artifacts\n",
    "        if len(sbps) < SEGLEN * 0.5 * 0.9:\n",
    "            #print('sbp nan > 10%')\n",
    "            continue\n",
    "        elif len(dbps) < SEGLEN * 0.5 * 0.9:\n",
    "            #print('dbp nan > 10%')\n",
    "            continue\n",
    "        elif len(mbps) < SEGLEN * 0.5 * 0.9:\n",
    "            #print('mbp nan > 10%')\n",
    "            continue\n",
    "        elif len(arts) < SEGLEN * SRATE * 0.9:\n",
    "            #print('art nan > 10%')\n",
    "            continue\n",
    "        elif np.max(mbps) > 200 or np.min(mbps) < 30:\n",
    "            #print('mbp max > 200 or mbp min < 30')\n",
    "            continue\n",
    "        elif np.max(arts) > 200 or np.min(arts) < 30:\n",
    "            #print('art max > 200 or art min < 30')\n",
    "            continue\n",
    "        elif (np.abs(np.diff(mbps)) > 10).any():\n",
    "            #print('abs diff > 10')\n",
    "            continue\n",
    "\n",
    "        raw_sbps_list.append(raw_sbps)\n",
    "        raw_dbps_list.append(raw_dbps)\n",
    "        raw_mbps_list.append(raw_mbps)\n",
    "        raw_arts_list.append(raw_arts)\n",
    "\n",
    "        case_sbps.append(np.max(sbps))\n",
    "        case_dbps.append(np.min(dbps))\n",
    "        case_mbps.append(np.mean(mbps))\n",
    "        case_art_sbps.append(np.max(arts))\n",
    "        case_art_dbps.append(np.min(arts))\n",
    "        case_art_mbps.append(np.mean(arts))\n",
    "\n",
    "    if len(case_sbps) < 10:\n",
    "        continue\n",
    "    elif len(case_dbps) < 10:\n",
    "        continue\n",
    "    elif len(case_mbps) < 10:\n",
    "        continue\n",
    "    \n",
    "    all_sbps.extend(case_sbps)\n",
    "    all_dbps.extend(case_dbps)\n",
    "    all_mbps.extend(case_mbps)\n",
    "    all_art_sbps.extend(case_art_sbps)\n",
    "    all_art_dbps.extend(case_art_dbps)\n",
    "    all_art_mbps.extend(case_art_mbps)\n",
    "\n",
    "    err_sbp = np.mean(np.abs(np.array(case_sbps) - np.array(case_art_sbps)))\n",
    "    err_dbp = np.mean(np.abs(np.array(case_dbps) - np.array(case_art_dbps)))\n",
    "    err_mbp = np.mean(np.abs(np.array(case_mbps) - np.array(case_art_mbps)))\n",
    "    err_sbp_perc = 100 * err_sbp / np.mean(case_sbps)\n",
    "    err_dbp_perc = 100 * err_dbp / np.mean(case_dbps)\n",
    "    err_mbp_perc = 100 * err_mbp / np.mean(case_mbps)\n",
    "\n",
    "    # if err_sbp_perc > 10 or err_dbp_perc > 10 or err_mbp_perc > 10:\n",
    "    #     imgdir = 'imgs/' + str(caseid)\n",
    "    #     if not os.path.exists(imgdir):\n",
    "    #         os.makedirs(imgdir)\n",
    "\n",
    "    #     for i in range(len(raw_sbps_list)):\n",
    "    #         plt.figure(figsize=(20,5))\n",
    "    #         plt.plot(raw_sbps_list[i], 'go')\n",
    "    #         plt.plot(raw_dbps_list[i], 'go')\n",
    "    #         plt.plot(raw_mbps_list[i], 'go')\n",
    "    #         plt.plot(raw_arts_list[i], 'r-')\n",
    "    #         plt.savefig(imgdir + \"/\" + str(i))\n",
    "    #         plt.close()\n",
    "\n",
    "    output = '{}\\t{}\\t{:.1f}\\t{:.1f}%\\t{:.1f}\\t{:.1f}%\\t{:.1f}\\t{:.1f}%\\n'.format(\n",
    "        caseid, len(case_sbps), err_sbp, err_sbp_perc, err_dbp, err_dbp_perc, err_mbp, err_mbp_perc)\n",
    "    fo.write(output)\n",
    "    print(output, end='')\n",
    "\n",
    "fo.close()"
   ]
  }
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